1. Field of the Invention
This invention relates to computer-assisted reasoning. More specifically, the invention provides a system and method for exploring solutions to complex problems by automatically generating multiple series of experiments, by permitting users to visualize outcomes of experiments, and by allowing users to easily generate additional series of experiments using differing input dimensions or solution strategies.
2. Description of the Related Art
People increasingly use computer-based calculations to help reason about complex problems in many fields, from scientific research to engineering design to business operations and planning. Yet, despite enormous improvements in computer capabilities over past decades, people still generally use computers to support the same style of quantitative reasoning they used before computers existed.
Even so, existing computer applications often perform calculations which produce results not obvious to the computer user. Such calculations can be considered experiments. The following are examples of experiments: a simulation model which projects the future course of the economy, a spreadsheet which combines disparate accounts into a profit and loss statement, a statistical routine which finds a pattern in a large database of chemical reaction outcomes (data mining,) a search over networked databases or the World Wide Web, and computer controlled physical experiments such as combining two chemicals to measure their reactivity. In contrast, a word processing program that displays keystrokes and places them into a computer file or a numerically controlled machine are not experiments.
Some computer applications perform several iterations of an experiment. For instance, many optimization routines can be used to make repeated calls to a simulation model in order to find the inputs which give the most desirable output. To date, however, users generally use computers to conduct iterations of experiments that are simple to specify analytically, such as by an optimization routine.
Recent studies suggested that a number of important, very difficult, and previously intractable problems can be approached by conducting a series of experiments directed to the problem. Such series of experiments, or compound experiments, can be extremely difficult and time-consuming to design and build. Thus, it has been exceedingly difficult to exploit the power of compound experiments, and the benefits have been difficult to perceive. Because of the prohibitive costs, it is simply not recognized how profoundly systematic compound experiments can enhance the analysis of previously intractable problems.
For example, computer models of the future economy and climate have been developed to answer questions phrased as optimization problems, such as, “what is the most cost-effective policy response, assuming we can accurately characterize the future behavior of the climate and economy over the next century.” While these models contain much useful information, they unfortunately lack the power to make accurate predictions in the face of numerous uncertainties and overwhelming complexity. Moreover, such direct policy questions may have no resolvable answer.
On the other hand, compound experiments can be used to devise robust policies that provide reasonably good results, no matter what predictions of the future turn out to be true. This sort of approach has produced useful results in isolated cases for various business and public policy problems.
Compound experiments are a crucial tool for supporting the reasoning that can lead to understanding very complex systems. To date, however, the great cost in time and human labor to construct compound experiments has largely limited their use to only the simplest problems. Moreover, there has simply been no support for conducting large numbers of experiments automatically driven by the needs of a specific reasoning strategy.
What is needed is a general-purpose mechanism that allows the construction of a wide variety of systems that support reasoning about complex and uncertain problems using compound experiments.